Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Light Acquisition02:16

Light Acquisition

8.5K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Turbulent Aggregation and Deposition Mechanism of Respirable Dust Pollutants under Wet Dedusting using a Two-Fluid Model with the Population Balance Method.

International journal of environmental research and public health·2019
Same author

The Involvement of Descending Pain Inhibitory System in Electroacupuncture-Induced Analgesia.

Frontiers in integrative neuroscience·2019
Same author

Direct modification of polyketone resin for anion exchange membrane of alkaline fuel cells.

Journal of colloid and interface science·2019
Same author

Palladium-Catalyzed Site-Selective C(sp<sup>3</sup>)-H Arylation of Phenylacetaldehydes.

Organic letters·2019
Same author

Electrochemical Oxidation of 5-Hydroxymethylfurfural on Nickel Nitride/Carbon Nanosheets: Reaction Pathway Determined by In Situ Sum Frequency Generation Vibrational Spectroscopy.

Angewandte Chemie (International ed. in English)·2019
Same author

Chiral Phosphoric-Acid-Catalyzed Cascade Prins Cyclization.

Organic letters·2019

相关实验视频

Updated: Jun 27, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.3K

有效的残留网络使用高光谱图像用于玉米品种识别.

Xueyong Li1, Mingjia Zhai1, Liyuan Zheng2

  • 1School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.

Frontiers in plant science
|May 1, 2024
PubMed
概括

一个高效的残余网络 (ERNet) 使用深度学习准确识别高光谱玉米种子. 这种方法达到98.36%的准确性,推进智能农业和种子质量控制.

关键词:
道的注意力 道的注意力种植作物种类 种植作物种类深度学习是一种深度学习.超光谱图像的使用线性差异分析线性差异分析

更多相关视频

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

16.2K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

相关实验视频

Last Updated: Jun 27, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.3K
RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
11:37

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Published on: August 8, 2017

16.2K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

科学领域:

  • 农业科学 农业科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 准确识别玉米种子品种和质量对于农业生产至关重要,影响种植管理,品种改进和质量控制.
  • 传统的手动分类方法不足以满足智能农业的需求.
  • 深度学习为农业应用提供先进的计算方法.

研究的目的:

  • 开发一种高效的深度学习模型,用于识别高光谱玉米种子.
  • 提高玉米种子分类的准确性和效率,超越传统方法.
  • 通过先进的图像分析来支持智能农业.

主要方法:

  • 使用线性差异分析的高光谱玉米种子图像的尺寸缩小.
  • 在深度学习网络中使用有效的残余块从图像中提取特征.
  • 使用软max分类器对高光谱玉米种子图像进行分类和检测.
  • 实施一个高效的残余网络 (ERNet),适用于高光谱图像分析.

主要成果:

  • 拟议的ERNet模型与其他深度学习技术和传统方法相比,表现优越.
  • 在识别高光谱玉米种子方面,ERNet实现了98.36%的高准确率.
  • 该方法有效地提取精细粒度的特征,这对于精确的种子分类至关重要.

结论:

  • ERNet提供了一种高效准确的解决方案,用于高光谱玉米种子识别.
  • 通过ERNet实现的高精度为未来涉及超光谱图像的分类研究提供了有价值的参考.
  • 这种深度学习方法有助于推进智能农业和精准农业实践.